Under this framework, the number of clusters, K, needs to be set. Most deep clustering methods assume \(K\)  is known [1]}, [2]}, [3]}. However, in a purely unsupervised approach, the number of clusters is not known. Various methods outside of the deep clustering literature exist to estimate \(K\) , including the average silhouette method [4]}, which we evaluate in our simulation study, among others [5]}, [6]}, [7]}. Furthermore, in the deep clustering framework, there are two times at which the optimal number of clusters could be estimated: (1) after the pre-training but before the clustering phase, or (2) after the clustering phase. We explore the performance of estimating \(K\)  at both of the times in our simulation section.
